Despite revolutionizing cancer treatment, immunotherapy demonstrates limited efficacy in a significant subset of patients, particularly those with negative conventional biomarkers, such as microsatellite stable (MSS) tumors. While high tumor mutational burden (TMB) and PD-L1 expression have emerged as valuable biomarkers for predicting response to immune checkpoint inhibitors (ICIs), a substantial proportion of cancers lacking these markers remain unresponsive. This underscores the urgent need to identify novel predictive biomarkers and develop innovative therapeutic strategies to overcome resistance mechanisms in this challenging patient population.

This Research Topic focuses on the emerging landscape of immunotherapy response prediction and treatment optimization in these cases, moving beyond the established paradigms of MSI, TMB, and PD-L1. We invite researchers and clinicians to contribute original research articles, insightful reviews, and illustrative case reports that address the following key areas:

Novel Biomarkers of ICI Response: Investigations into promising predictive and prognostic biomarkers beyond MSI, TMB and PD-L1, including but not limited to:
• Tumor microenvironment (TME) characteristics (e.g., immune cell infiltration, cytokine profiles)
• Genomic alterations and molecular subtypes
• Epigenetic modifications
• Gut microbiome composition
• Systemic immune status and circulating biomarkers

Overcoming Immunotherapy Resistance: Exploration of innovative therapeutic approaches to enhance ICI efficacy, with a particular focus on:
• Rational combination therapies (e.g., ICIs combined with targeted therapies, antibody-drug conjugates, epigenetic modulators, oncolytic viruses)
• Strategies to modulate the TME and enhance immune cell infiltration
• Personalized treatment approaches based on individual tumor and host characteristics

Are you interested in contributing a manuscript to this Research Topic?

Beyond Conventional Biomarkers: Unlocking Immunotherapy Response Through Novel Biomarkers or Combinatorial Approaches